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Dual-branch spectral-spatial feature extraction network for multispectral image compression

MULTIMEDIA SYSTEMS 2023年第6期29卷 3579-3597页

作者： Kong, Fanqiang Tang, Jiahui Li, Yunsong Li, Dan Hu, Kedi Nanjing Univ Aeronaut & Astronaut Coll Astronaut Nanjing 210016 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China

The advent of the information age and the continuous development of spectrum imaging technologies have both triggered an explosion of multispectral image information data. Hence, image data need to be compressed first no matter transmitting or storing information. This paper designs an end-to-end compression algorithm based on spectral-spatial domain feature leaning (SSDFL). SSDFL consists of dual branches: one to obtain spectral features by Bi-ConvLSTM, and the other for spatial feature extraction via residual multiscale depth-wise convolutions (RMDCs). The attention mechanism is conductive to ulteriorly fuse features. The encoder with SSDFL serves to extract important features from multispectral images and decrease their size to 1/8 of its original size. After decreasing dimensions of images, the quantizer turns the latent floating-point characteristics into integer data. The following phase utilizes lossless entropy coding to execute coding on the integer input and estimate the bitstream. The rate-distortion is added to the training process in order to jointly optimize the image bitstream and loss. The decoder is nearly symmetrical to the encoder to restore high-quality images. Two datasets from the WorldView-3 and Setinel-2 satellites are adopted in experiment with PSNR, MS-SSIM, SS, and MSA as evaluation indexes. Corresponding results prove that the performance of the proposed approach is superior to that of traditional compression algorithms 3D-SPIHT and JPEG2000. In addition, compared with deep learning compression methods CNN-based, ConvGRU-based and Partitioned, the suggested method still outperforms at various bitrates.

关键词： multispectral image compression Spectral-spatial feature extraction Bidirectional convolutional long short-term memory Depth-wise convolution Rate-distortion

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Efficient end-to-end multispectral image compression 6

Efficient end-to-end multispectral image compression

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Conference on Big Data VI - Learning, Analytics, and Applications

作者： Depoian, Arthur C., II Bailey, Colleen P. Guturu, Parthasarathy Univ North Texas Dept Elect Engn Denton TX 76207 USA

ISBN: (纸本)9781510673915;9781510673908

multispectral imagery is instrumental across diverse domains, including remote sensing, environmental monitoring, agriculture, and healthcare, as it offers a treasure trove of data over various spectral bands, enabling profound insights into our environment. However, with the ever-expanding volume of multispectral data, the need for efficient compression methods is becoming increasingly critical. Enhanced compression not only conserves precious storage space, but also facilitates rapid data transmission and analysis, ensuring the accessibility of vital information. In particular, in applications such as satellite imaging, where bandwidth constraints and storage limitations are prevalent, superior compression techniques are essential to minimize costs and maximize resource utilization. Neural network-based compression methods are emerging as a solution to address this escalating challenge. While autoencoders have become a common neural network approach to image compression, they face limitations in generating customized quantization maps for training images, relying on feature extraction. However, the integration of bespoke quantization maps alongside feature extraction can elevate compression performance to levels previously considered unattainable. The concept of end-to-end image compression, encompassing both quantization maps and feature extraction, offers a comprehensive approach to represent an image in its simplest form. The proposed method considers not only the compression ratio and image quality but also the substantial computational costs associated with current approaches. Designed to capitalize on similarities within and across spectral channels, it ensures accurate reproduction of the original source information, promising a more efficient and effective solution for multispectral image compression.

关键词： multispectral image compression Remote Sensing Hyperspectral Imaging Deep Learning/Machine Learning Artificial Intelligence image Processing

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A channel-gained single-model network with variable rate for multispectral image compression in UAV air-to-ground remote sensing

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MULTIMEDIA SYSTEMS 2024年第4期30卷 193-193页

作者： Wang, Wei Zhu, Daiyin Hu, Kedi Nanjing Univ Aeronaut & Astronaut Informat Technol Ctr Informationizat Dept Nanjing 211106 Jiangsu Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 211106 Jiangsu Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Astronaut Nanjing 211106 Jiangsu Peoples R China

Unmanned aerial vehicle (UAV) air-to-ground remote sensing technology, has the advantages of long flight duration, real-time image transmission, wide applicability, low cost, and so on. To better preserve the integrity of image features during transmission and storage, and improve efficiency in the meanwhile, image compression is a very important link. Nowadays the image compressor based on deep learning framework has been updating as the technological development. However, in order to obtain enough bit rates to fit the performance curve, there is always a severe computational burden, especially for multispectral image compression. This problem arises not only because the complexity of the algorithm is deepening, but also repeated training with rate-distortion optimization. In this paper, a channel-gained single-model network with variable rate for multispectral image compression is proposed. First, a channel gained module is introduced to map the channel content of the image to vector domain as amplitude factors, which leads to representation scaling, as well as obtaining the image representation of different bit rates in a single model. Second, after extracting spatial-spectral features, a plug-and-play dynamic response attention mechanism module is applied to take good care of distinguishing the content correlation of features and weighting the important area dynamically without adding extra parameters. Besides, a hyperprior autoencoder is used to make full use of edge information for entropy estimation, which contributes to a more accurate entropy model. The experiments prove that the proposed method greatly reduces the computational cost, while maintaining good compression performance and surpasses JPEG2000 and some other algorithms based on deep learning in PSNR, MSSSIM and MSA.

关键词： UAV air-to-ground remote sensing multispectral image compression Variable rate Hyperprior autoencoder

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Deep Inter-spectrum Prediction Network for multispectral image compression 10th

Deep Inter-spectrum Prediction Network for Multispectral Ima...

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10th International Conference on Communications, Signal Processing, and Systems

作者： Meng, Yuxin Hu, Kedi Cao, Tongbo Chen, Mengyue Nanjing Univ Aeronaut & Astronaut Coll Astronaut Nanjing 210016 Peoples R China

ISBN: (纸本)9789811903908;9789811903892

multispectral image is one of the information carriers, which contains rich spatial and spectral information, and uncompressed multispectral images pose huge challenges for the storage and transmission of information. With the appearance and development of traditional image compression methods such as JPEG, JPEG2000 and 3D-SPIHT, image compression has exerted its performance to the extreme but brings block artifact and algorithm complexity. With the rapid development of deep learning, a deep inter-spectrum prediction network is proposed in this paper using the strong spatial and spectral correlation of multispectral image, which can enhance the reconstruction image quality. In proposed framework, the decoder `D' uses spatial information extracted by the conditioning network `Cond' and the spectral information extracted by the inter-spectrum encoder `E' to predict original image. Our proposed approach is compared with JPEG2000 and 3D-SPIHT in objective evaluation index PSNR. And the experimental results show that our proposed framework have the significant quality improvements.

关键词： multispectral image compression Convolutional neural network Inter-spectrum prediction

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Spectral-Spatial Feature Completely Separated Extraction with Tensor CNN for multispectral image compression 10th

Spectral-Spatial Feature Completely Separated Extraction wit...

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10th International Conference on Communications, Signal Processing, and Systems

作者： Cao, Tongbo Zhang, Ning Zhao, Shunmin Hu, Kedi Wang, Kang Nanjing Univ Aeronaut & Astronaut Coll Astronaut Nanjing 210016 Peoples R China AVIC Xian Flight Automat Control Res Inst Facri Natl Key Lab Sci & Technol Aircraft Control Xian 710065 Peoples R China

ISBN: (纸本)9789811903908;9789811903892

Considering the rich spectral and spatial information of multispectral image, a learned multispectral image compression method named spectral-spatial feature completely separated extraction with tensor CNN is proposed. In encoder, two parallel modules, spectral module and spatial module, are applied to extract spectral and spatial features respectively. Then in order to reduce the loss in the downsampling process, two tensor layers that integrate tucker decomposition and neural network are utilized to decompose the multiway features. The decomposed feature tensors are concatenated along the channel dimension, saving the spectral and spatial features separately. To recover the features, the concatenated tensor is split in symmetric decoder after quantizer and entropy codec. Finally, postprocessing module is utilized to remove the compression artifacts of reconstructed image fused by recovered feature tensors. Besides, rate-distortion optimization is embedded to balance the trade-off between the rate loss and the distortion. Experiments on eight-band dataset from the WorldView-3 satellite demonstrate our proposed method outperforms JPEG2000 and 3D-SPIHT in PSNR.

关键词： multispectral image compression Spectral-spatial feature Completely separated extraction Tensor layer Rate-distortion optimization

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Spatial-spectral feature deep extraction based on a multichannel grouping fusion module for multispectral image compression

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JOURNAL OF ELECTRONIC IMAGING 2022年第3期31卷 033024-033024页

作者： Kong, Fangqiang Wang, Kang Li, Dan Hu, Kedi Nanjing Univ Aeronaut & Astronaut Coll Astronaut Nanjing Peoples R China

With the continuous improvement of spatial and spectral resolution, the application of multispectral images has greatly increased in remote sensing. However, the amount of image data also increases sharply, which brings great pressure to data storage and transmission. To solve this issue, we propose an end-to-end image compression scheme according to spatial- spectral feature extraction, which can be implemented by a spatial-spectral memory unit (SSMU). Furthermore, to improve the feature extraction capability of this deep network, the multichannel grouping fusion module is adopted to reconstruct and fuse the image features. In the encoder of the proposed compression scheme, the SSMU first extracts spatial-spectral features along the spatial direction and the spectral direction, and the multichannel grouping fusion module extracts the spatial and spectral features of different levels by recombination and fusion of band features of multispectral images. Then, the extracted deep spatial and spectral features are compressed by downsampling. Next, the quantizer and entropy coding convert the data into a compressed bitstream. In the decoder, a reverse process is used to restore the original images. The experiments take the multispectral images of Landsat 8 and WorldView3 as the datasets to verify the superiority of our method and compare it with JPEG2000, 3D-SPIHT, and the CNN-based methods. The results show that the proposed method outperforms the JPEG2000, 3D-SPIHT, and CNN-based methods in PSNR, spectral similarity, and spectral angle mapping metrics at different bit rates. (C) 2022 SPIE and IS&T

关键词： deep learning multispectral image compression end-to-end compression lasers spatial-spectral features

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End-to-end multispectral image compression framework based on adaptive multiscale feature extraction

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JOURNAL OF ELECTRONIC IMAGING 2021年第1期30卷 013010-013010页

作者： Kong, Fanqiang Zhao, Shunmin Li, Yunsong Li, Dan Nanjing Univ Aeronut & Astronaut Coll Astronaut Nanjing Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian Peoples R China

multispectral image compression can considerably reduce the volume of data and promote their application. However, conventional single-scale compression schemes, such as JPEG2000 and three-dimensional set partitioning in hierarchical tree (3D-SPIHT), do not accurately preserve the features of images due to the complex features of multispectral images. A compression framework based on adaptive multiscale feature extraction with a convolutional neural network is proposed. First, an adaptive multiscale feature extraction module, which is the basic component of the compression framework, is designed to extract the multiscale spatial-spectral features of the multispectral images and adaptively adjust the weights of the features according to the content of the images. Second, the encoder, which is composed of multiscale feature extraction modules, extracts the multiscale spatial-spectral features of the multispectral images, and the extracted features are quantized and encoded by the quantizer and the entropy coder to generate a compressed bitstream. Third, the decoder, which is structurally similar to the encoder, is utilized to recover the images. The rate-distortion optimizer is embedded in the encoder to control the trade-off between the rate loss and the distortion. The results of these experiments on multispectral images of the Landsat 8 satellite and the WorldView-3 satellite validate the better performance of our compression framework compared with the performances of conventional schemes, including JPEG2000 and 3D-SPIHT. In order to further verify the effectiveness of multiscale features, the framework is compared with a single-scale compression algorithm based on deep learning, the experimental results validate that the performance of the single-scale compression algorithm superior to the conventional schemes but inferior to our multiscale algorithm, which indicates that the multiscale features can significantly improve the performance of the compression algorit

关键词： multispectral image compression multiscale features convolutional neural network rate-distortion optimizer

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Spectral-Spatial Feature Partitioned Extraction Based on CNN for multispectral image compression

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REMOTE SENSING 2021年第1期13卷 9-9页

作者： Kong, Fanqiang Hu, Kedi Li, Yunsong Li, Dan Zhao, Shunmin Nanjing Univ Aeronaut & Astronaut Coll Astronaut Nanjing 210016 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China

Recently, the rapid development of multispectral imaging technology has received great attention from many fields, which inevitably involves the image transmission and storage problem. To solve this issue, a novel end-to-end multispectral image compression method based on spectral-spatial feature partitioned extraction is proposed. The whole multispectral image compression framework is based on a convolutional neural network (CNN), whose innovation lies in the feature extraction module that is divided into two parallel parts, one is for spectral and the other is for spatial. Firstly, the spectral feature extraction module is used to extract spectral features independently, and the spatial feature extraction module is operated to obtain the separated spatial features. After feature extraction, the spectral and spatial features are fused element-by-element, followed by downsampling, which can reduce the size of the feature maps. Then, the data are converted to bit-stream through quantization and lossless entropy encoding. To make the data more compact, a rate-distortion optimizer is added to the network. The decoder is a relatively inverse process of the encoder. For comparison, the proposed method is tested along with JPEG2000, 3D-SPIHT and ResConv, another CNN-based algorithm on datasets from Landsat-8 and WorldView-3 satellites. The result shows the proposed algorithm outperforms other methods at the same bit rate.

关键词： spectral-spatial feature multispectral image compression partitioned extraction group convolution rate-distortion

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MultiTempGAN: Multitemporal multispectral image compression framework using generative adversarial networks

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JOURNAL OF VISUAL COMMUNICATION AND image REPRESENTATION 2021年 81卷 103385-103385页

作者： Karaca, Ali Can Kara, Ozan Gullu, Mehmet Kemal Yildiz Tech Univ Dept Comp Engn Istanbul Turkey Kocaeli Univ Dept Elect & Commun Engn Kocaeli Turkey Izmir Bakircay Univ Dept Elect & Elect Engn Izmir Turkey

multispectral satellites that measure the reflected energy from the different regions on the Earth generate the multispectral (MS) images continuously. The following MS image for the same region can be acquired with respect to the satellite revisit period. The images captured at different times over the same region are called multitemporal images. Traditional compression methods generally benefit from spectral and spatial correlation within the MS image. However, there is also a temporal correlation between multitemporal images. To this end, we propose a novel generative adversarial network (GAN) based prediction method called MultiTempGAN for compression of multitemporal MS images. The proposed method defines a lightweight GAN-based model that learns to transform the reference image to the target image. Here, the generator parameters of MultiTempGAN are saved for the reconstruction purpose in the receiver system. Due to MultiTempGAN has a low number of parameters, it provides efficiency in multitemporal MS image compression. Experiments were carried out on three Sentinel-2 MS image pairs belonging to different geographical regions. We compared the proposed method with JPEG2000-based conventional compression methods and three deep learning methods in terms of signal-tonoise ratio, mean spectral angle, mean spectral correlation, and laplacian mean square error metrics. Additionally, we have also evaluated the change detection performances and visual maps of the methods. Experimental results demonstrate that MultiTempGAN not only achieves the best metric values among the other methods at high compression ratios but also presents convincing performances in change detection applications.

关键词： multispectral image compression Generative adversarial networks Big data Remote sensing Multitemporal images

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A residual network framework based on weighted feature channels for multispectral image compression

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AD HOC NETWORKS 2020年 107卷 102272-102272页

作者： Kong, Fanqiang Zhao, Shunmin Li, Yunsong Li, Dan Zhou, Yongbo Nanjing Univ Aeronaut & Astronaut Coll Astronaut Nanjing 210016 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China

Deep learning has achieved great success in many computer vision tasks, such as recognition, image enhancement and image compression. However, it is difficult to use a residual network that can efficiently consider the characteristics of multispectral images for multispectral image compression. In this paper, a novel end-to-end multispectral image compression framework based on a weighted feature channel residual network is proposed to efficiently remove the spatial and spectral redundancy of multispectral images by extracting the importance of each channel. The multispectral image compression framework includes a forward coding network, a rate-distortion optimizer, a quantizer/inverse quantizer, an entropy encoder/decoder and an inverse decoding network. In the encoder, multispectral images are directly fed into the forward coding network, and the main spectral and spatial features of the multispectral images are extracted by the residual block. Additionally, the weighted feature channel module can explicitly model the relationship between feature channels when extracting features from multispectral images and adaptively allocate different weights for each feature channel through training. The rate-distortion optimizer is added to make the main features compact. Then, the intermediate feature data are quantized and encoded by lossless entropy coding to obtain a code stream. In the decoder, the code stream is approximately restored to the intermediate features through the entropy decoder and the inverse quantizer. Then, the intermediate features are reconstructed to the multispectral images by the inverse decoding network. Experimental results on 7-band multispectral images of the Landsat 8 satellite and 8-band multispectral images of WorldView-3 satellite demonstrate that the proposed algorithm can achieve a better PSNR than conventional 2D schemes (which are JPEG20 0 0 and JPEG in this paper) and 3D scheme (which is 3D-SPIHT in this paper) and can effectively pres

关键词： Deep learning Residual network Weighted feature channels multispectral image compression

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